Sampling-Based Optimization for Multi-Agent Model Predictive Control

TL;DR

This paper reviews sampling-based optimization methods for multi-agent control, extending them with distributed algorithms using consensus ADMM, and demonstrates their effectiveness on complex vehicle and quadcopter simulations.

Contribution

It introduces distributed sampling-based MPC algorithms for multi-agent systems, combining stochastic search with consensus ADMM for parallel optimization.

Findings

Distributed algorithms outperform centralized ones in large-scale scenarios.

Proposed methods effectively control multi-agent vehicle and quadcopter systems.

Scalability demonstrated on a 196-vehicle simulation.

Abstract

We systematically review the Variational Optimization, Variational Inference and Stochastic Search perspectives on sampling-based dynamic optimization and discuss their connections to state-of-the-art optimizers and Stochastic Optimal Control (SOC) theory. A general convergence and sample complexity analysis on the three perspectives is provided through the unifying Stochastic Search perspective. We then extend these frameworks to their distributed versions for multi-agent control by combining them with consensus Alternating Direction Method of Multipliers (ADMM) to decouple the full problem into local neighborhood-level ones that can be solved in parallel. Model Predictive Control (MPC) algorithms are then developed based on these frameworks, leading to fully decentralized sampling-based dynamic optimizers. The capabilities of the proposed algorithms framework are demonstrated on multiple complex multi-agent tasks for vehicle and quadcopter systems in simulation. The results compare different distributed sampling-based optimizers and their centralized counterparts using unimodal Gaussian, mixture of Gaussians, and stein variational policies. The scalability of the proposed distributed algorithms is demonstrated on a 196-vehicle scenario where a direct application of centralized sampling-based methods is shown to be prohibitive.